DataSeer Snapshot is an AI integration that provides desperately needed editorial automation for open science: scanning manuscripts for transparent research practices such as data sharing, code sharing, and pre-registration. SnapShot works within existing service provider- and journal- workflows, allowing journal staff to rapidly triage manuscripts, moving those that display signals of research integrity into peer review faster, and automatically providing guidance when it’s needed. Snapshot helps researchers to fulfill policy requirements more easily and frees editors to do their best work, improving the quality and output of the published scientific version of record.
Rigorous requirements mean rigorous editorial checks
Along with peer review, editorial checks are key to supporting high quality research and upholding research integrity. But editorial checks take time, attention, and significant research expertise. As the scholarly community embraces detailed reporting standards, data sharing requirements, and other transparent practices, those checks only become more complex and demanding. Through a detailed analysis of editorial checks, and using DataSeer’s existing technology, we’ve been able to develop Snapshot as an iThenticate-like automated check.
“Publishers and industry service providers are carrying out more and more detailed checks related to data, code, and transparent research practices, such as linking to methods and protocols,” said DataSeer founder and CEO Tim Vines. “The time required for those editorial checks is always balanced against author-service factors like time to first decision. Anyone who’s ever worked in a journal office knows the pain of manuscript backlogs.”
How SnapShot can help
DataSeer SnapShot uses natural language processing and AI technologies to automate research integrity checks, reducing the administrative burden for journals and empowering editors to provide targeted author support when needed. The checks answer questions like whether data has been deposited in a trusted repository in a robust and actionable way.
SnapShot integrates seamlessly with the platforms and systems publishers already rely on to track submissions and orchestrate peer review. It scans manuscripts in seconds and scores each on transparent research practices such as data sharing, code sharing, protocol sharing, and adherence to reporting standards. Those results can inform manuscript triage workflows, or trigger automated feedback to authors offering helpful instructions and advice.
“Everyone needs a prompt sometimes,” said DataSeer Partnership director Adrian Stanley. “Think of how your email system alerts you when a message mentions an attachment, but doesn’t include a file. That’s exactly the kind of intelligent guidance SnapShot provides—but for broken links and missing data files—whatever transparent research practices you identify.”
Honoring researchers who demonstrate research integrity
Supporting reproducible research requires more than just enforcing policy—celebrating successes and showcasing the positive impact of transparent practices is just as important. SnapShot identifies the manuscripts that display indicators of reproducibility and research integrity at scale and before publication.
Journals can leverage that information in myriad ways:
- Comparing high- and low-scoring manuscripts to identify patterns and begin getting out in front of potential issues with proactive resources and training
- Featuring high-scoring articles in press releases, social media, and email highlights
- Providing positive reinforcement to the authors of high-scoring articles, building goodwill and encouraging future resubmission
- Tapping high-scoring authors for speaking opportunities, or roles in peer review, editorial board service, or mentorship programs
Freeing editors up for the work only they can do
Streamlining editorial checks means more time for the real editorial work—things only a caring and skilled editor can do, such as securing great content, guiding authors through a revision process, and building strong editorial boards.
“Here at DataSeer, we’ve been automating this type of detailed analysis for many years now,” said Vines. “It’s a natural progression from providing transparency data on published manuscripts through Open Science Metrics to now prescreening with SnapShot.”
DataSeer takes care of the routine screening processes, so editors can do what they do best.
